Volume visualization of a tomographic reconstruction is greatly handicapped by the presence of noise. Averaging techniques, which are usually applied to reduce noise in single particle (repeatable structures) work, cannot be used for tomographic reconstructions of individual non-repeatable structures. Noise reduction based on Wavelet transformation, along with a nonlinear filtration of the transform coefficients has proved to be better than the conventional filter techniques, such as the median filter or the Wiener filter. In periodic signals, Fourier analysis is the optimum transformation to separate signal and noise. However in non-periodic signals, Wavelet transformation seems to be the most efficient in this regard. In image data, structural elements are characterized by orientation and extension and are transformed to a small number of large coefficients, whereas noise being devoid of these characteristics are transformed into a large number of small coefficients. Based on a suitable nonlinear thresholding technique these small coefficients are set to zero to get rid of the noise. In a nutshell, this proposed technique of de-noising a tomographic reconstruction comprises of doing a Wavelet transformation of the reconstructed volume and using a non-linear filter in the transform domain to minimize noise to improve the final visualization. The strength of de-noising and the preservation of structural features are controlled by the size of threshold parameter. More work is needed to devise a model to calculate the correct threshold parameter. Currently, a threshold parameter of twice the median, taken from the moduli of transform coefficients has been reported to work perfectly (based on few actual and test volumes). However, these results need to be checked when applied to a large number of different reconstructed volumes. A literature survey on Wavelet transforms and this new technique for de noising the tomographic reconstructions has been made. Efforts are being made to understand these techniques. Hardware and software requirements for implementation of this technique have been assessed. Arrangements are in progress to install the required software in our laboratory.